Short-term interval prediction modeling of photovoltaic power plant output power based on combing Least Squares Method and weighted Markov chain
The paper examines a new method for photovoltaic power plant output power forecast modeling, in order to adapt to the impacts of new energy penetrated power system, realize the grid planning and operation of photovoltaic power generation system. It expands on the condition classify of photovoltaic p...
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| Veröffentlicht in: | RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2016-12 (E12), p.363-373 |
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| creator | Hu, Bo Piao, Zailin Zhou, Dongsheng Guo, Dan Wang, Zheyuan |
| description | The paper examines a new method for photovoltaic power plant output power forecast modeling, in order to adapt to the impacts of new energy penetrated power system, realize the grid planning and operation of photovoltaic power generation system. It expands on the condition classify of photovoltaic power plant output power by taking full advantage of the matched curve of Least Squares Method, it builds the Markov chain model, and calculates the coefficients and weights of each order autocorrelation, it carries on the weighted Markov chain forecast modeling by means of combining the correlation analysis and Markov chain. The numerical results of Liaoning Jinzhou 3MW PV plant indicate that, the state interval division is reasonable, predictive analysis is accurate. The interval forecast of photovoltaic power plant output power supplies reliable basis for interval optimization modeling of uncertain variables after new energy into the grid. The method is easy to implement, it is more efficient use of time, and provides a broader outlook. Keywords: Least Squares Method; Weighted Markov chain; Interval forecast; Autocorrelation coefficient; Interval Optimization |
| format | Article |
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It expands on the condition classify of photovoltaic power plant output power by taking full advantage of the matched curve of Least Squares Method, it builds the Markov chain model, and calculates the coefficients and weights of each order autocorrelation, it carries on the weighted Markov chain forecast modeling by means of combining the correlation analysis and Markov chain. The numerical results of Liaoning Jinzhou 3MW PV plant indicate that, the state interval division is reasonable, predictive analysis is accurate. The interval forecast of photovoltaic power plant output power supplies reliable basis for interval optimization modeling of uncertain variables after new energy into the grid. The method is easy to implement, it is more efficient use of time, and provides a broader outlook. Keywords: Least Squares Method; Weighted Markov chain; Interval forecast; Autocorrelation coefficient; Interval Optimization</description><identifier>ISSN: 1646-9895</identifier><language>eng</language><publisher>Lousada: AISTI (Iberian Association for Information Systems and Technologies)</publisher><subject>Alternative energy sources ; Electric power generation ; Electricity distribution ; Least squares method ; Markov analysis ; Markov chains ; Photovoltaic cells ; Power plants ; Renewable resources ; Short term ; Variables</subject><ispartof>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação, 2016-12 (E12), p.363-373</ispartof><rights>COPYRIGHT 2016 AISTI (Iberian Association for Information Systems and Technologies)</rights><rights>Copyright Associação Ibérica de Sistemas e Tecnologias de Informacao Dec 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Hu, Bo</creatorcontrib><creatorcontrib>Piao, Zailin</creatorcontrib><creatorcontrib>Zhou, Dongsheng</creatorcontrib><creatorcontrib>Guo, Dan</creatorcontrib><creatorcontrib>Wang, Zheyuan</creatorcontrib><title>Short-term interval prediction modeling of photovoltaic power plant output power based on combing Least Squares Method and weighted Markov chain</title><title>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</title><description>The paper examines a new method for photovoltaic power plant output power forecast modeling, in order to adapt to the impacts of new energy penetrated power system, realize the grid planning and operation of photovoltaic power generation system. It expands on the condition classify of photovoltaic power plant output power by taking full advantage of the matched curve of Least Squares Method, it builds the Markov chain model, and calculates the coefficients and weights of each order autocorrelation, it carries on the weighted Markov chain forecast modeling by means of combining the correlation analysis and Markov chain. The numerical results of Liaoning Jinzhou 3MW PV plant indicate that, the state interval division is reasonable, predictive analysis is accurate. The interval forecast of photovoltaic power plant output power supplies reliable basis for interval optimization modeling of uncertain variables after new energy into the grid. The method is easy to implement, it is more efficient use of time, and provides a broader outlook. Keywords: Least Squares Method; Weighted Markov chain; Interval forecast; Autocorrelation coefficient; Interval Optimization</description><subject>Alternative energy sources</subject><subject>Electric power generation</subject><subject>Electricity distribution</subject><subject>Least squares method</subject><subject>Markov analysis</subject><subject>Markov chains</subject><subject>Photovoltaic cells</subject><subject>Power plants</subject><subject>Renewable resources</subject><subject>Short term</subject><subject>Variables</subject><issn>1646-9895</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptjctqwzAQRb1ooSHNPwi6dpEtyZaXIfQFCV2kXRtZHtlKbcmR5OQ3-slVaaBddGZx4XLOzFWyyApapBWv2E2y8v6A4zBMKp4vks99b11IA7gRaRPjJAY0OWi1DNoaNNoWBm06ZBWaehvsyQ5BaIkmewaHpkGYgOwcpjlcqkZ4aFFUpR2bb3MLwge0P87CgUc7CL1tkTAtOoPu-hDhnXAf9oRkL7S5Ta6VGDysLrlM3h8f3jbP6fb16WWz3qZdVrCQcs5KLGSjOFWFrLKy5DhraEt5LpkCIgsOVDDeZIRmHFQlC4wFqfKm5UwRRpbJ3c_dydnjDD7UBzs7E1_WOc5JUbIyJ79UJwaotVE2OCFH7WW9pmVFcYEJjdT9P1TcFkYtrQGlY_9H-AJyAX50</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Hu, Bo</creator><creator>Piao, Zailin</creator><creator>Zhou, Dongsheng</creator><creator>Guo, Dan</creator><creator>Wang, Zheyuan</creator><general>AISTI (Iberian Association for Information Systems and Technologies)</general><general>Associação Ibérica de Sistemas e Tecnologias de Informacao</general><scope>INF</scope><scope>3V.</scope><scope>7XB</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CLZPN</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20161201</creationdate><title>Short-term interval prediction modeling of photovoltaic power plant output power based on combing Least Squares Method and weighted Markov chain</title><author>Hu, Bo ; Piao, Zailin ; Zhou, Dongsheng ; Guo, Dan ; Wang, Zheyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g165t-88570acbf84f6c9177801b4d482c5fe3c68e4a58b13418ef9c600a392bd85f353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Alternative energy sources</topic><topic>Electric power generation</topic><topic>Electricity distribution</topic><topic>Least squares method</topic><topic>Markov analysis</topic><topic>Markov chains</topic><topic>Photovoltaic cells</topic><topic>Power plants</topic><topic>Renewable resources</topic><topic>Short term</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Bo</creatorcontrib><creatorcontrib>Piao, Zailin</creatorcontrib><creatorcontrib>Zhou, Dongsheng</creatorcontrib><creatorcontrib>Guo, Dan</creatorcontrib><creatorcontrib>Wang, Zheyuan</creatorcontrib><collection>Gale OneFile: Informe Academico</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Latin America & Iberia Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Bo</au><au>Piao, Zailin</au><au>Zhou, Dongsheng</au><au>Guo, Dan</au><au>Wang, Zheyuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Short-term interval prediction modeling of photovoltaic power plant output power based on combing Least Squares Method and weighted Markov chain</atitle><jtitle>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</jtitle><date>2016-12-01</date><risdate>2016</risdate><issue>E12</issue><spage>363</spage><epage>373</epage><pages>363-373</pages><issn>1646-9895</issn><abstract>The paper examines a new method for photovoltaic power plant output power forecast modeling, in order to adapt to the impacts of new energy penetrated power system, realize the grid planning and operation of photovoltaic power generation system. It expands on the condition classify of photovoltaic power plant output power by taking full advantage of the matched curve of Least Squares Method, it builds the Markov chain model, and calculates the coefficients and weights of each order autocorrelation, it carries on the weighted Markov chain forecast modeling by means of combining the correlation analysis and Markov chain. The numerical results of Liaoning Jinzhou 3MW PV plant indicate that, the state interval division is reasonable, predictive analysis is accurate. The interval forecast of photovoltaic power plant output power supplies reliable basis for interval optimization modeling of uncertain variables after new energy into the grid. The method is easy to implement, it is more efficient use of time, and provides a broader outlook. Keywords: Least Squares Method; Weighted Markov chain; Interval forecast; Autocorrelation coefficient; Interval Optimization</abstract><cop>Lousada</cop><pub>AISTI (Iberian Association for Information Systems and Technologies)</pub><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1646-9895 |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Alternative energy sources Electric power generation Electricity distribution Least squares method Markov analysis Markov chains Photovoltaic cells Power plants Renewable resources Short term Variables |
| title | Short-term interval prediction modeling of photovoltaic power plant output power based on combing Least Squares Method and weighted Markov chain |
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